What You Need to Know — and Ignore — about Machine Reasoning

Recently, several technology companies have briefed me and
professed to use a new type of artificial intelligence (AI) technology: machine
reasoning.

If you haven’t heard the
term yet, just wait. You’re going to be soon seeing it everywhere.

When I first heard the
pitches, I asked if they meant machine learning, but were merely
using a different term to distinguish themselves.

“No,” they assured me,
“machine reasoning is completely different.”

As an industry analyst,
this, of course, set me on a path to understanding the ins-and-outs of this
apparently new technology, to separate the hype from reality, and to determine
if this new technology was, in fact, the next big thing.

In short — and as is
often the case — it’s a little bit hype and a little bit reality.

Machine reasoning is a
legitimately developing segment of the broader AI sector. But it’s also
incredibly nascent and will undoubtedly be subject to the frothy, over-hyped
marketing treatment that has beset all things AI and digital transformation.

To help you prepare for the coming onslaught of machine reasoning hype and hyperbole, here’s what you need to know — and ignore — about it.

The Limits of Machine Learning

Machine Learning is one
of the most mature, broadly applicable, and production-ready forms of AI
presently available. Organizations from across the spectrum of industries are
applying it to significant result across a wide range of use cases.

As you might expect,
therefore, technology companies are embedding it into virtually every category
of software. In fact, it’s getting difficult to find a piece of modern software
that doesn’t include at least some form of the technology.

Machine learning has become table stakes.

But while it is proving
to be versatile and powerful, it also comes with some substantial requirements.
And they are beginning to limit its usefulness — at least as the appetite for
more sophisticated intelligent applications grows.

Effectively applying
machine learning requires massive amounts of data so that it can uncover the patterns
that are central to its operation. Moreover, machine learning is inherently
deterministic, even in its unsupervised learning form — it only works when you
have a pre-determined problem, inputs, and expected outputs.

For many of the early
machine learning use cases, this wasn’t a problem. Organizations knew precisely
what questions they were trying to answer, and they had the data in which to
find the patterns that held the answers. They just needed a machine that could
expose them quickly, predictably, and automatically.

But what if you don’t
have enough data to make machine learning work? Or even worse, what if you’re
not even sure what questions you need to answer to solve a particular problem.

It is in these types of
situations that you would turn to machine reasoning.

“Machine reason is the
concept of giving machines the power to make connections between facts,
observations, and all the magical things that we can train machines to do with
machine learning.”

In effect, machine
reasoning is about making a machine approach information the same way humans
do: by understanding its essence so that it can use that understanding to
process and comprehend data that would otherwise have no context. Or, as the
same Cognilytica article put it, “to functionally use that information for higher
ends, or apply learning from one domain to another without human involvement.”

From a very young age,
children apply both inductive and deductive reasoning in just this way as they
learn. For instance, tell a small child that all birds have feathers, and then
separately explain that the parakeet he’s looking at is a bird, and he will
rightly reason that all parakeets have feathers.

A machine learning
algorithm, however, will not. It is unable to connect otherwise unrelated
datasets to come to such a conclusion.

The usefulness of a
machine reasoning system, therefore, becomes readily apparent in any situation
in which you have only limited data or in which that data is highly volatile
such that you cannot build a reliable machine learning model based upon it.

Likewise, problems that
have no directly correlated dataset are problematic for machine learning
algorithms as it’s impossible to detect patterns in data when you don’t know
which data to analyze.

A human, of course,
would use reasoning to deduce the potential sources of the problem, and then
examine the relevant datasets, identifying relationships between them as she
went until a possible answer emerged. She would then test that answer and start
the process over again if it didn’t fit.

While this approach
would be impossible for a machine learning model that requires a defined
dataset, it’s precisely the type of problem that machine reasoning can solve.

The only problem is that
it doesn’t really exist. Yet.

Beware Machine Reasoning Hype

Despite that fact that
we’re starting to see machine reasoning pop-up in messaging and marketing
materials, the reality is that true machine reasoning is not yet here.

Being able to infer
context requires conceptual domain awareness — the ability to intuit what may
be relevant and what may not be in any given situation — that is proving
elusive to codify in machine reasoning models, except in very narrow-banded use
cases.

Likewise, while the
computational demands of machine learning are still pushing the boundaries of
currently available, affordable, and scalable resources, machine reasoning is
“easily one order or more of complexity beyond machine learning,” according to
Cognilytica.

Still, that doesn’t mean
that the tech companies that are beginning to use the machine reasoning moniker
are just blowing hot air. These companies are, in fact, applying elements of
machine reasoning approaches to address the machine learning gaps.

The challenge, of
course, is that to accomplish this feat, they must apply these approaches in
very narrow and targeted use cases — those in which they can significantly
narrow and define the universe of potential relationships and contextual
domains.

These early applications
are showing promise in their ability to predict outcomes or events with limited
and loosely-related datasets, but under the covers, machine learning algorithms
are still doing much of the heavy lifting.

The Intellyx Take: Don’t Get Swept Up

I expect that you will
hear a lot about machine reasoning in the coming months and years.

Tech company marketers
are always on the hunt for anything that will help differentiate them in a
crowded and noisy market. And the allure of a new technology that can solve new
problems and work with limited data will be just too juicy for most of them to
pass up.

And, as is almost always
the case, there will be elements of truth to the pitch. They will have new,
patented approaches that help solve specific problems better, faster, and less
expensively.

So what’s a progressive
enterprise leader to do?

If a particular machine
reasoning-based solution helps you solve a pressing business problem with which
you’ve been struggling, then great! Swipe right, make a match, and go for it.

Otherwise, take care not to get caught up in the next great wave of hype. True machine reasoning is likely some ways off and — trust me on this one — you’ll know it when it gets here. Until then, remain steadfastly focused on one thing: how to harness technology to delight your customers, partners, and employees, and create a competitive advantage in the process. Whether that comes in the form of a machine reasoning solution or not is beside the point.